/*3. Line graph: Make sure that you specify gender for your name to correctly create the line graph*/
/*4. Create a percent variable by gender and name. We've already dropped anyone with a lower rank than
our name. Therefore our denominator is simply the remaining sample.
Use the command "summarize" to help you answer this question. */
/*5. Create a percent variable by name. What is the probability of someone having
your name today - I meant 2014. And again, this is after you've already
filtered the data. Therfore your denominator will be on the sample of people
who had at least as popular names as yours.
Again, use "summarize" to show this information. */
/*8. Reshape the data such that you have separate columns for male and female counts
For examples on this see: http://www.belenchavez.com/teaching/week-2 */
/* 10. declare your data to be panel dataset. Before you encode, do the following.
This is assuming you reshaped on the sex_str variable which is why the counts
here are countF countM. If you reshaped on the fem variable, your variables
would be named count0 count1:*/
drop if decade<1940
drop if countF<=50 & countM==0
drop if countM<=50 & countF==0
/*13. Create a percent change variable. Make sure to specify gender. So, if I have a
percent change variable called pct_change and my countF denotes count of females, I'd type: */
list name decade countF pct_change if name =="Belen"
/* From there I could answer 14.*/